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From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Neural Networks (DDCNN)

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From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Neural Networks (DDCNN)

Data-efficient learning in continuous state-action spaces using high-dimensional observations remains a elusive challenge in developing fully autonomous systems. An instance of this challenge is the pixels to torques problem, which identifies key elements of an autonomous agent: autonomous thinking and decision making using sensor measurements only, learning from mistakes, and applying past experiences to novel situations. In this research, we introduce a deep dynamical convolutional model, able to learn complex non-linear dynamics and do long-term predictions. Compared to state-of-the-art reinforcement learning methods for continuous state and action space problems, our approach is solid and efficient as it is model-based, is scalable to high-dimensional state spaces, learns quickly, and is a major step towards fully autonomous learning from pixels to torques.

Bibtex

@article{assael2015data,
  title={Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models},
  author={Assael, J.-A. M and Wahlstr{\"o}m, N. and Sch{\"o}n, T. B. and Deisenroth, M. P.},
  journal={NIPS Deep Reinforcement Learning Workshop},
  year={2015}
}

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Copyright (C) 2015 John-Alexander M. Assael, Marc P. Deisenroth

The MIT License (MIT)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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